Unity 机器学习代理工具包 (ML-Agents) 是一个开源项目,它使游戏和模拟能够作为训练智能代理的环境。
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5.5 KiB

# # Unity ML-Agents Toolkit
import logging
from multiprocessing import Process, Queue
import numpy as np
from docopt import docopt
from .trainer_controller import TrainerController
from .exception import TrainerError
def run_training(sub_id, run_seed, run_options, process_queue):
"""
Launches training session.
:param process_queue: Queue used to send signal back to main.
:param sub_id: Unique id for training session.
:param run_seed: Random seed used for training.
:param run_options: Command line arguments for training.
"""
# Docker Parameters
docker_target_name = (run_options['--docker-target-name']
if run_options['--docker-target-name'] != 'None' else None)
# General parameters
env_path = (run_options['--env']
if run_options['--env'] != 'None' else None)
run_id = run_options['--run-id']
load_model = run_options['--load']
train_model = run_options['--train']
save_freq = int(run_options['--save-freq'])
keep_checkpoints = int(run_options['--keep-checkpoints'])
worker_id = int(run_options['--worker-id'])
curriculum_file = (run_options['--curriculum']
if run_options['--curriculum'] != 'None' else None)
lesson = int(run_options['--lesson'])
fast_simulation = not bool(run_options['--slow'])
no_graphics = run_options['--no-graphics']
trainer_config_path = run_options['<trainer-config-path>']
# Create controller and launch environment.
tc = TrainerController(env_path, run_id + '-' + str(sub_id),
save_freq, curriculum_file, fast_simulation,
load_model, train_model, worker_id + sub_id,
keep_checkpoints, lesson, run_seed,
docker_target_name, trainer_config_path, no_graphics)
# Signal that environment has been launched.
process_queue.put(True)
# Begin training
tc.start_learning()
def main():
try:
print('''
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''')
except:
print('\n\n\tUnity Technologies\n')
logger = logging.getLogger('mlagents.trainers')
_USAGE = '''
Usage:
mlagents-learn <trainer-config-path> [options]
mlagents-learn --help
Options:
--env=<file> Name of the Unity executable [default: None].
--curriculum=<directory> Curriculum json directory for environment [default: None].
--keep-checkpoints=<n> How many model checkpoints to keep [default: 5].
--lesson=<n> Start learning from this lesson [default: 0].
--load Whether to load the model or randomly initialize [default: False].
--run-id=<path> The directory name for model and summary statistics [default: ppo].
--num-runs=<n> Number of concurrent training sessions [default: 1].
--save-freq=<n> Frequency at which to save model [default: 50000].
--seed=<n> Random seed used for training [default: -1].
--slow Whether to run the game at training speed [default: False].
--train Whether to train model, or only run inference [default: False].
--worker-id=<n> Number to add to communication port (5005) [default: 0].
--docker-target-name=<dt> Docker volume to store training-specific files [default: None].
--no-graphics Whether to run the environment in no-graphics mode [default: False].
'''
options = docopt(_USAGE)
logger.info(options)
num_runs = int(options['--num-runs'])
seed = int(options['--seed'])
if options['--env'] == 'None' and num_runs > 1:
raise TrainerError('It is not possible to launch more than one concurrent training session '
'when training from the editor.')
jobs = []
run_seed = seed
for i in range(num_runs):
if seed == -1:
run_seed = np.random.randint(0, 10000)
process_queue = Queue()
p = Process(target=run_training, args=(i, run_seed, options, process_queue))
jobs.append(p)
p.start()
# Wait for signal that environment has successfully launched
while process_queue.get() is not True:
continue